My primary work is in the Learning Robot Laboratory, attempting to
develop robots which learn from experience and in the real
world. Initially I focussed on reinforcement learning: an inductive
method for learning control strategies based on delayed reward. A
tangible contribution of this work was developing one of the first
applications of ``reinforcement learning'' from vision in a real
world complex domain (that is, to fixate and approach a cup). While
seeing that such learning techniques could be successful in acquiring
control strategies for simple robot tasks, currently we are
researching learning techniques which will scale up to more complex
and realistic tasks.

This first step in this research was studying
Explanation-based Neural Network Learning
.
This technique unifies neural network and explanation-based
learning approaches, combining the advantages of inductive and
analytical learning techniques. Our robot test-bed wanders the
corridors of CMU, inductively learning action models which are then
used as a domain theory in analytically learning corridor concepts
(that is, hallways, junctions etc) based upon functional
definitions. The sensation of the world contains both vision and
sonar information, with the fusing of these modalities being part
of the learning task.

The robot testbed in the Learning Robot Lab, Xavier, was designed
and constructed internally within the laboratory in 1993. As one of
the members of this process and of the subsequent software
development for/entry into the various AAAI Robotic Competitions, I
maintain a practical secondary interest in lowlevel aspects of
robotics (operating systems, device drivers, etc) -- all an
important consideration for long-life autonomous robots.

Joseph O'Sullivan.``The CMU Learning Robot Laboratory Data Toolkit'' In
Proceedings of the MLC-Colt '94 Workshop on Robot Learning.
Rutgers, The State University of New Jersey, New Brunswick, July 9, 1994.
[Abstract][Postscript]

Tom M. Mitchell, Joseph O'Sullivan and Sebastian Thrun.
``Explanation-Based Learning for Mobile Robot Perception''. In
Proceedings of the MLC-Colt '94 Workshop on Robot Learning .
Rutgers, The State University of New Jersey, New Brunswick, July 9, 1994.
[Abstract][Postscript]